Another approach to imbalanced learning involves separating a neural network into feature extraction and classification stages. Leading methods in this area include: Open Long Tailed Recognition (OLTR) [1]}, the Bilateral Branch Network (BBN) [2]}, and Decoupling Recognition and Classifier for Long-Tailed Recognition (Decoupling) [3]}. BBN employs a learning branch, a re-balancing branch and a weighted cross-entropy loss to re-train the classifier. The Decoupling technique uses a variety of methods to re-train a classifier, including: random re-initalization and optimization of the weights, computation of the mean feature representation of each class, and direct adjustment of the classifier norms. Our CNN training framework for imbalanced data takes inspiration from the above work on model decoupling. Our approach, however, includes a specialized data augmentation process in the learned embedded space and classifier fine-tuning that efficiently reduces the generalization gap.
